MATLAB Image Restoration Techniques

This program provides fundamental content for MATLAB-based image restoration techniques, primarily covering the following areas:

1. Introduction to Image Restoration: Detailed explanation of image restoration concepts, principles, and common methods. Through this introduction, readers can understand the basic principles and methodologies of image restoration, including implementation approaches using MATLAB's Image Processing Toolbox functions like deconvwnr for Wiener filtering.

2. Image Noise Models: Explanation of common noise types in images, such as Gaussian noise and salt-and-pepper noise, along with methods for establishing appropriate noise models for image restoration. Includes MATLAB code examples for generating and analyzing different noise patterns using imnoise function with specific parameters.

3. Spatial Domain Filtering Restoration: Introduction to image restoration methods in the spatial domain, such as mean filtering and median filtering. Through studying these filtering restoration methods, readers can learn how to use filtering techniques to remove noise from images, with practical implementations using MATLAB's medfilt2 and filter2 functions for different kernel sizes.

4. Comprehensive Image Restoration Methods: Integration of various image restoration techniques to propose a comprehensive restoration approach that can more accurately restore images corrupted by noise. Demonstrates advanced MATLAB implementations combining multiple algorithms like adaptive filtering and frequency domain restoration for optimal results.

By studying this program, readers can master the fundamental knowledge of MATLAB-based image restoration techniques and apply this knowledge to practical image restoration operations, including hands-on experience with MATLAB's built-in functions and custom algorithm development.